CiteLLM：一个用于可信科学引文发现的代理平台
CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery
AI总结：
CiteLLM通过在LaTeX编辑器中嵌入LLM工具，实现可信的科学引文发现，确保引文的准确性和可靠性。
摘要：
大型语言模型（LLMs）创造了增强学术活动效率的新机遇；然而，在AI辅助的伦理部署中仍存在挑战，包括（1）AI生成内容的可信度，（2）学术诚信和知识产权的保护，以及（3）信息隐私的保护。在本工作中，我们提出了CiteLLM，一个专门的代理平台，旨在通过 grounding 作者起草的声明和说法来实现可信的引文发现。该系统通过将LLM工具直接嵌入LaTeX编辑器环境中，引入了一种新的交互范式，确保无缝的用户体验和没有数据传输到本地系统之外。为保证无幻觉的引文，我们采用动态纪律意识路由，仅从受信任的基于网络的学术存储库中检索候选者，同时利用LLM仅生成上下文感知的搜索查询，按相关性排序候选者，并通过段落级语义匹配和集成聊天机器人验证和解释支持。评估结果表明，所提出系统在返回有效且高度可用的引文方面表现优异。
英文摘要：
Large language models (LLMs) have created new opportunities to enhance the efficiency of scholarly activities; however, challenges persist in the ethical deployment of AI assistance, including (1) the trustworthiness of AI-generated content, (2) preservation of academic integrity and intellectual property, and (3) protection of information privacy. In this work, we present CiteLLM, a specialized agentic platform designed to enable trustworthy reference discovery for grounding author-drafted claims and statements. The system introduces a novel interaction paradigm by embedding LLM utilities directly within the LaTeX editor environment, ensuring a seamless user experience and no data transmission outside the local system. To guarantee hallucination-free references, we employ dynamic discipline-aware routing to retrieve candidates exclusively from trusted web-based academic repositories, while leveraging LLMs solely for generating context-aware search queries, ranking candidates by relevance, and validating and explaining support through paragraph-level semantic matching and an integrated chatbot. Evaluation results demonstrate the superior performance of the proposed system in returning valid and highly usable references.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
